SUMMARYThis article presents a low quiescent current output-capacitorless quasi-digital complementary metal-oxidesemiconductor (CMOS) low-dropout (LDO) voltage regulator with controlled pass transistors according to load demands. The pass transistor of the LDO is segmented into two smaller sizes based on a proposed segmentation criterion, which considers the maximum output voltage transient variations due to the load transient to different load current steps to find the suitable current boundary for segmentation. This criterion shows that low load conditions will cause more output variations and settling time if the pass transistor is used in its maximum size. Furthermore, this situation is the worst case for stability requirements of the LDO. Therefore, using one smaller transistor for low load currents and another one larger for higher currents, a proper trade-off between output variations, complexity, and power dissipation is achieved. The proposed LDO regulator has been designed and post-simulated in HSPICE in a 0.18 μm CMOS process to supply a stable load current between 0 and 100 mA with a 40 pF on-chip output capacitor, while consuming 4.8 μA quiescent current. The dropout voltage of the LDO is set to 200 mV for 1.8 V input voltage. The results reveal an improvement of approximately 53% and 25% on the output voltage variations and settling time, respectively.

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This paper presents an output-capacitorless class-AB low-dropout (LDO) regulator with load current sinking and sourcing ability. The proposed LDO consists of two complementary pass transistors, controlled using a level shifter technique. The transient improvement section applied to the gates of the pass devices enhances the transient performance of the LDO. The proposed LDO is designed in TSMC 0.18 μm CMOS process with input and output voltages of 1.2-2.5 V and 1 V, respectively, 10 pF output capacitor, and quiescent current of 3.14 μA, and is capable to sink and source maximum load currents of ±100 mA, giving the current efficiency of 99.99%.

The statistical processing of sensor data using conventional digital computers is inefficient in terms of time, energy usage, and communication bandwidth, among others. Therefore, new approaches are sought to create context and make sense of the sensor data using special‐purpose computers that excel in specific computation tasks. Herein, the requirements for physical systems to perform sophisticated nonlinear computations needed for real‐time pattern recognition in data, specifically sensor data, are discussed. The focus is on physical reservoir computing as a neuromorphic computing approach. Considering energy flow as the coupling mechanism between nonlinear dynamic systems, it is demonstrated that many physical systems satisfy the basic requirements for building reservoir computers. Using physical reservoir computers brings up exciting opportunities for near‐ or in‐sensor computing as to how new data are collected and processed. The concepts are demonstrated through a novel physical computation platform, where off‐the‐shelf, temperature‐sensitive resistors are used to perform various standard and specific computational tasks. This platform is used as a near‐sensor processor to detect particular events. How a similar platform may be used for in‐sensor neuromorphic computations is further discussed.

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